{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-05-21T13:41:07.927776700Z",
     "start_time": "2024-05-21T13:41:07.816998Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小: 1070\n",
      "验证集大小: 31\n",
      "测试集大小: 363\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense\n",
    "from keras.preprocessing.sequence import TimeseriesGenerator\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 设置中文字体和负号正常显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 读取CSV文件  \n",
    "df = pd.read_csv(\"./Lingcang202001-202312.csv\")\n",
    "\n",
    "# 将year, month, day合并为日期，并转换为datetime类型  \n",
    "df['date'] = pd.to_datetime(df[['year', 'month', 'day']])\n",
    "\n",
    "# 删除原始的year, month, day列  \n",
    "df.drop(['year', 'month', 'day'], axis=1, inplace=True)\n",
    "\n",
    "# 按照日期排序，确保数据的连续性  \n",
    "df.sort_values('date', inplace=True)\n",
    "\n",
    "# 设置日期为索引  \n",
    "df.set_index('date', inplace=True)\n",
    "\n",
    "# 划分训练集和验证集  \n",
    "# 训练集：2020年1月1日至2022年11月30日  \n",
    "# 验证集：2022年12月1日至2022年12月31日  \n",
    "train_end_date = \"2022-11-30\"\n",
    "validation_start_date = \"2022-12-01\"\n",
    "validation_end_date = \"2022-12-31\"\n",
    "test_start_date = \"2023-01-01\"\n",
    "\n",
    "train_df = df.loc[df.index <= train_end_date]\n",
    "validation_df = df.loc[(df.index >= validation_start_date) & (df.index <= validation_end_date)]\n",
    "test_df = df.loc[df.index >= test_start_date]\n",
    "\n",
    "# 打印训练集和验证集的大小  \n",
    "print(f\"训练集大小: {len(train_df)}\")\n",
    "print(f\"验证集大小: {len(validation_df)}\")\n",
    "print(f\"测试集大小: {len(test_df)}\")\n",
    "\n",
    "# 如果需要重置索引，可以使用以下代码（可选）  \n",
    "# train_df.reset_index(inplace=True, drop=True)  \n",
    "# validation_df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75ba0ae79f941f00",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:54:20.791472Z",
     "start_time": "2024-05-21T13:41:07.906780300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "Epoch 1/50\n",
      "1063/1063 [==============================] - 17s 15ms/step - loss: 0.0134\n",
      "Epoch 2/50\n",
      "1063/1063 [==============================] - 17s 16ms/step - loss: 0.0076\n",
      "Epoch 3/50\n",
      "1063/1063 [==============================] - 17s 16ms/step - loss: 0.0074\n",
      "Epoch 4/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0069\n",
      "Epoch 5/50\n",
      "1063/1063 [==============================] - 17s 16ms/step - loss: 0.0069\n",
      "Epoch 6/50\n",
      "1063/1063 [==============================] - 17s 16ms/step - loss: 0.0066\n",
      "Epoch 7/50\n",
      "1063/1063 [==============================] - 17s 16ms/step - loss: 0.0065\n",
      "Epoch 8/50\n",
      "1063/1063 [==============================] - 17s 16ms/step - loss: 0.0066\n",
      "Epoch 9/50\n",
      "1063/1063 [==============================] - 17s 16ms/step - loss: 0.0066\n",
      "Epoch 10/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0065\n",
      "Epoch 11/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0065\n",
      "Epoch 12/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0064\n",
      "Epoch 13/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0064\n",
      "Epoch 14/50\n",
      "1063/1063 [==============================] - 15s 15ms/step - loss: 0.0064\n",
      "Epoch 15/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0064\n",
      "Epoch 16/50\n",
      "1063/1063 [==============================] - 15s 14ms/step - loss: 0.0062\n",
      "Epoch 17/50\n",
      "1063/1063 [==============================] - 15s 14ms/step - loss: 0.0062\n",
      "Epoch 18/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0062\n",
      "Epoch 19/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0063\n",
      "Epoch 20/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0062\n",
      "Epoch 21/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0062\n",
      "Epoch 22/50\n",
      "1063/1063 [==============================] - 15s 14ms/step - loss: 0.0062\n",
      "Epoch 23/50\n",
      "1063/1063 [==============================] - 15s 14ms/step - loss: 0.0061\n",
      "Epoch 24/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0061\n",
      "Epoch 25/50\n",
      "1063/1063 [==============================] - 15s 15ms/step - loss: 0.0062\n",
      "Epoch 26/50\n",
      "1063/1063 [==============================] - 15s 14ms/step - loss: 0.0062\n",
      "Epoch 27/50\n",
      "1063/1063 [==============================] - 15s 14ms/step - loss: 0.0062\n",
      "Epoch 28/50\n",
      "1063/1063 [==============================] - 14s 13ms/step - loss: 0.0062\n",
      "Epoch 29/50\n",
      "1063/1063 [==============================] - 15s 14ms/step - loss: 0.0062\n",
      "Epoch 30/50\n",
      "1063/1063 [==============================] - 15s 14ms/step - loss: 0.0062\n",
      "Epoch 31/50\n",
      "1063/1063 [==============================] - 14s 14ms/step - loss: 0.0061\n",
      "Epoch 32/50\n",
      "1063/1063 [==============================] - 14s 14ms/step - loss: 0.0061\n",
      "Epoch 33/50\n",
      "1063/1063 [==============================] - 15s 14ms/step - loss: 0.0061\n",
      "Epoch 34/50\n",
      "1063/1063 [==============================] - 14s 13ms/step - loss: 0.0060\n",
      "Epoch 35/50\n",
      "1063/1063 [==============================] - 14s 13ms/step - loss: 0.0061\n",
      "Epoch 36/50\n",
      "1063/1063 [==============================] - 15s 14ms/step - loss: 0.0062\n",
      "Epoch 37/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0061\n",
      "Epoch 38/50\n",
      "1063/1063 [==============================] - 17s 16ms/step - loss: 0.0061\n",
      "Epoch 39/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0061\n",
      "Epoch 40/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0061\n",
      "Epoch 41/50\n",
      "1063/1063 [==============================] - 17s 16ms/step - loss: 0.0061\n",
      "Epoch 42/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0061\n",
      "Epoch 43/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0061\n",
      "Epoch 44/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0061\n",
      "Epoch 45/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0060\n",
      "Epoch 46/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0060\n",
      "Epoch 47/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0061\n",
      "Epoch 48/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0060\n",
      "Epoch 49/50\n",
      "1063/1063 [==============================] - 17s 16ms/step - loss: 0.0060\n",
      "Epoch 50/50\n",
      "1063/1063 [==============================] - 16s 15ms/step - loss: 0.0060\n",
      "Validation Loss: 0.011789021082222462\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_1 (LSTM)               (None, 50)                10400     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 51        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10,451\n",
      "Trainable params: 10,451\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Scaler saved to ./lstm_scaler.joblib\n",
      "模型已保存为LSTM_temperature_prediction_model.h5\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# 数据预处理  \n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_train_data = scaler.fit_transform(train_df['average'].values.reshape(-1, 1))\n",
    "scaled_validation_data = scaler.transform(validation_df['average'].values.reshape(-1, 1))\n",
    "scaled_test_data = scaler.transform(test_df['average'].values.reshape(-1, 1))\n",
    "\n",
    "# 创建时序数据生成器  \n",
    "n_input = 7  # 使用前n_input个时间步预测下一个时间步  \n",
    "n_features = 1  # 特征数量，这里是平均气温  \n",
    "generator = TimeseriesGenerator(scaled_train_data, scaled_train_data, length=n_input, batch_size=1)\n",
    "\n",
    "# 构建LSTM模型  \n",
    "model = Sequential()  # 创建Sequential模型\n",
    "model.add(LSTM(50, activation='relu', input_shape=(n_input, n_features)))  # 添加LSTM层\n",
    "model.add(Dense(1))  # 添加全连接层\n",
    "model.compile(optimizer='adam', loss='mse')  # 编译模型\n",
    "\n",
    "# 训练模型  \n",
    "history = model.fit(generator, epochs=50, verbose=1, batch_size=16)  # 50个epoch, verbose=1表示输出训练过程\n",
    "\n",
    "# 验证模型  \n",
    "validation_x = []  # 用于存放验证集的输入数据\n",
    "validation_y = []  # 用于存放验证集的标签数据\n",
    "for i in range(n_input, len(scaled_validation_data)):  # 从第n_input个数据开始\n",
    "    validation_x.append(scaled_validation_data[i - n_input:i])  # 用前n_input个数据作为输入\n",
    "    validation_y.append(scaled_validation_data[i])  # 第n_input个数据作为标签\n",
    "validation_x = np.array(validation_x)  # 转换为NumPy数组\n",
    "validation_y = np.array(validation_y)  # 转换为NumPy数组\n",
    "\n",
    "validation_loss = model.evaluate(validation_x, validation_y, verbose=0)  # 计算验证集的损失\n",
    "print(f'Validation Loss: {validation_loss}')\n",
    "\n",
    "# 可视化模型的训练过程\n",
    "losses = history.history['loss'] # 从History对象中提取损失值  \n",
    "# 使用matplotlib绘制损失曲线  \n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(losses)\n",
    "plt.title('Training Loss over Epochs')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()\n",
    "# 如果需要，可以将预测结果反归一化回原始的温度范围  \n",
    "# predicted_temperatures = scaler.inverse_transform(model.predict(validation_x))\n",
    "\n",
    "# 输出模型的摘要信息\n",
    "model.summary()\n",
    "\n",
    "# 保存scaler  \n",
    "scaler_path = './lstm_scaler.joblib'  # 定义scaler保存的文件路径  \n",
    "joblib.dump(scaler, scaler_path)  # 使用joblib保存scaler对象  \n",
    "print(f\"Scaler saved to {scaler_path}\")\n",
    "\n",
    "# 保存模型\n",
    "model.save('LSTM_temperature_prediction_model.h5')\n",
    "print('模型已保存为LSTM_temperature_prediction_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d5125bf7949fafc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:54:20.817405600Z",
     "start_time": "2024-05-21T13:54:20.480383800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 132ms/step\n",
      "Predicted temperature for validation point 1: 13.78 ℃\n",
      "Predicted temperature for validation point 2: 13.80 ℃\n",
      "Predicted temperature for validation point 3: 13.22 ℃\n",
      "Predicted temperature for validation point 4: 12.59 ℃\n",
      "Predicted temperature for validation point 5: 12.59 ℃\n",
      "Predicted temperature for validation point 6: 13.04 ℃\n",
      "Predicted temperature for validation point 7: 13.38 ℃\n",
      "Predicted temperature for validation point 8: 13.00 ℃\n",
      "Predicted temperature for validation point 9: 12.34 ℃\n",
      "Predicted temperature for validation point 10: 13.34 ℃\n",
      "Predicted temperature for validation point 11: 11.03 ℃\n",
      "Predicted temperature for validation point 12: 10.92 ℃\n",
      "Predicted temperature for validation point 13: 11.24 ℃\n",
      "Predicted temperature for validation point 14: 11.37 ℃\n",
      "Predicted temperature for validation point 15: 12.04 ℃\n",
      "Predicted temperature for validation point 16: 12.10 ℃\n",
      "Predicted temperature for validation point 17: 12.35 ℃\n",
      "Predicted temperature for validation point 18: 11.80 ℃\n",
      "Predicted temperature for validation point 19: 12.33 ℃\n",
      "Predicted temperature for validation point 20: 12.58 ℃\n",
      "Predicted temperature for validation point 21: 12.47 ℃\n",
      "Predicted temperature for validation point 22: 10.98 ℃\n",
      "Predicted temperature for validation point 23: 11.29 ℃\n",
      "Predicted temperature for validation point 24: 11.83 ℃\n"
     ]
    }
   ],
   "source": [
    "# 假设model是之前训练好的LSTM模型，scaler是之前用于数据归一化的MinMaxScaler对象  \n",
    "\n",
    "# 使用模型对验证集进行预测  \n",
    "validation_x = []\n",
    "for i in range(n_input, len(scaled_validation_data)):\n",
    "    validation_x.append(scaled_validation_data[i - n_input:i])\n",
    "validation_x = np.array(validation_x)\n",
    "\n",
    "# 预测验证集  \n",
    "predictions_scaled = model.predict(validation_x)\n",
    "\n",
    "# 将预测结果反归一化回原始温度范围  \n",
    "predictions = scaler.inverse_transform(predictions_scaled)\n",
    "\n",
    "# 将预测结果输出到控制台或保存到文件中  \n",
    "for i, pred in enumerate(predictions):\n",
    "    print(f\"Predicted temperature for validation point {i + 1}: {pred[0]:.2f} ℃\") \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce228001135ab0ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:54:20.817405600Z",
     "start_time": "2024-05-21T13:54:20.657075Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 如果validation_y未反归一化，请取消注释以下行并进行反归一化  \n",
    "validation_y = scaler.inverse_transform(validation_y.reshape(-1, 1)).flatten()\n",
    "\n",
    "# 绘制真实值和预测值的对比图  \n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(validation_y, label='True Values')\n",
    "plt.plot(predictions.flatten(), label='Predictions')\n",
    "plt.title('True Temperatures vs Predicted Temperatures')\n",
    "plt.xlabel('Validation Points')\n",
    "plt.ylabel('Temperature')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dfdbad36c0eaa258",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:54:20.896343900Z",
     "start_time": "2024-05-21T13:54:20.781435200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 137.1579\n",
      "Root Mean Squared Error (RMSE): 11.7114\n",
      "Mean Absolute Error (MAE): 11.5703\n",
      "Mean Absolute Percentage Error (MAPE): 98.25%\n",
      "R2 Score: -39.8971\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "# 假设validation_y是验证集的真实值（且已经归一化）  \n",
    "# 如果validation_y未归一化，需要先使用scaler进行归一化  \n",
    "# validation_y = scaler.transform(validation_y_original.reshape(-1, 1))  \n",
    "\n",
    "# 计算MSE  \n",
    "mse = mean_squared_error(validation_y, predictions_scaled)\n",
    "print(f\"Mean Squared Error (MSE): {mse:.4f}\")\n",
    "\n",
    "# 计算RMSE  \n",
    "rmse = np.sqrt(mse)\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse:.4f}\")\n",
    "\n",
    "# 计算MAE  \n",
    "mae = mean_absolute_error(validation_y, predictions_scaled)\n",
    "print(f\"Mean Absolute Error (MAE): {mae:.4f}\")\n",
    "\n",
    "# 计算MAPE\n",
    "mape = np.mean(np.abs((validation_y - predictions_scaled) / validation_y)) * 100\n",
    "print(f\"Mean Absolute Percentage Error (MAPE): {mape:.2f}%\")\n",
    "\n",
    "# 计算R2\n",
    "from sklearn.metrics import r2_score\n",
    "r2 = r2_score(validation_y, predictions_scaled)\n",
    "print(f\"R2 Score: {r2:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e574616fcfea6a10",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:54:21.383682Z",
     "start_time": "2024-05-21T13:54:20.846433500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
      "12/12 [==============================] - 0s 4ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# 加载已保存的模型  \n",
    "from keras.models import load_model\n",
    "model = load_model('LSTM_temperature_prediction_model.h5')\n",
    "\n",
    "# 准备测试集数据  \n",
    "test_x = []  # 用于存放测试集的输入数据  \n",
    "test_y = []  # 存放测试集的真实标签数据（如果有的话，可用于对比）  \n",
    "\n",
    "for i in range(n_input, len(scaled_test_data)):  # 从第n_input个数据开始  \n",
    "    test_x.append(scaled_test_data[i - n_input:i])  # 用前n_input个数据作为输入  \n",
    "    test_y.append(scaled_test_data[i])  # 保存真实值用于对比（如果需要）  \n",
    "\n",
    "test_x = np.array(test_x)  # 转换为NumPy数组  \n",
    "test_y = np.array(test_y)  # 转换为NumPy数组（如果需要对比真实值）  \n",
    "\n",
    "# 创建一个从2023-01-01开始的日期序列，长度为test_y的长度  \n",
    "start_date = datetime(2023, 1, 1)\n",
    "dates = [start_date + timedelta(days=i) for i in range(len(test_y))]\n",
    "\n",
    "# 使用模型对测试集进行预测  \n",
    "predictions_scaled = model.predict(test_x)\n",
    "\n",
    "# 如果需要，将预测结果和测试集真实值反归一化回原始的温度范围  \n",
    "predictions = scaler.inverse_transform(predictions_scaled)  # 反归一化预测值  \n",
    "test_y_original = scaler.inverse_transform(test_y.reshape(-1, 1)).flatten()  # 反归一化真实值  \n",
    "\n",
    "# 绘制测试集真实值与预测值的对比图  \n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# 使用dates作为x轴的值  \n",
    "plt.plot(dates, test_y_original, label='True Values')  # 绘制真实值  \n",
    "plt.plot(dates, predictions, label='Predictions')  # 绘制预测值  \n",
    "\n",
    "# 设置x轴为日期格式  \n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "plt.gca().xaxis.set_major_locator(mdates.MonthLocator())  # 每个月显示一个刻度  \n",
    "plt.gcf().autofmt_xdate()  # 自动旋转日期标记  \n",
    "\n",
    "plt.xlim(datetime(2023, 1, 1), datetime(2023, 12, 31))  # 设置x轴范围\n",
    "plt.title('Test Set Predictions vs True Values in 2023')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Temperature')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73b6976bdfd333ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:54:21.383682Z",
     "start_time": "2024-05-21T13:54:21.365886500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 1.5858\n",
      "Root Mean Squared Error (RMSE): 1.2593\n",
      "Mean Absolute Error (MAE): 0.9363\n",
      "Mean Absolute Percentage Error (MAPE): 24.87%\n",
      "R2 Score: 0.8875\n"
     ]
    }
   ],
   "source": [
    "# 测试集模型评估\n",
    "# 计算MSE\n",
    "mse = mean_squared_error(test_y_original, predictions)\n",
    "print(f\"Mean Squared Error (MSE): {mse:.4f}\")\n",
    "\n",
    "# 计算RMSE\n",
    "rmse = np.sqrt(mse)\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse:.4f}\")\n",
    "\n",
    "# 计算MAE\n",
    "mae = mean_absolute_error(test_y_original, predictions)\n",
    "print(f\"Mean Absolute Error (MAE): {mae:.4f}\")\n",
    "\n",
    "# 计算MAPE\n",
    "mape = np.mean(np.abs((test_y_original - predictions) / test_y_original)) * 100\n",
    "print(f\"Mean Absolute Percentage Error (MAPE): {mape:.2f}%\")\n",
    "\n",
    "# 计算R2\n",
    "r2 = r2_score(test_y_original, predictions)\n",
    "print(f\"R2 Score: {r2:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "64d231007508e20e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-21T13:54:21.476471200Z",
     "start_time": "2024-05-21T13:54:21.377702300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 21ms/step\n",
      "Predicted temperature for 2023-05-20 00:00:00: 17.70 ℃\n"
     ]
    }
   ],
   "source": [
    "# 假设 target_date 是你想要预测的日期  \n",
    "target_date = datetime(2023, 5, 20)\n",
    "n_input = 7  # 假设模型是基于过去7天的数据来预测下一天  \n",
    "\n",
    "# previous_data 应该包含 target_date 之前 n_input 天的数据，且已经归一化  \n",
    "# 这里只是一个示例，你需要用实际的数据替换它  \n",
    "previous_data = np.random.rand(n_input)  # 假设数据，需要替换为真实数据  \n",
    "\n",
    "# 将数据构造成模型所需的输入格式  \n",
    "input_data = previous_data.reshape(1, n_input, 1)  # 假设模型输入形状为 (batch_size, time_steps, features)  \n",
    "\n",
    "# 使用模型进行预测  \n",
    "predictions_scaled = model.predict(input_data)\n",
    "\n",
    "# 反归一化预测结果  \n",
    "predictions = scaler.inverse_transform(predictions_scaled)\n",
    "\n",
    "print(f\"Predicted temperature for {target_date}: {predictions[0][0]:.2f} ℃\")"
   ]
  }
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